Project | Paper-AAAI (Download the paper [here] in case the AAAI link is broken) | Poster

project │ README.md | run.sh - main script to train CAIN model | run_noca.sh - script to train CAIN_NoCA model | test_custom.sh - script to run interpolation on custom dataset | eval.sh - script to evaluate on SNU-FILM benchmark | main.py - main file to run train/val | config.py - check & change training/testing configurations here | loss.py - defines different loss functions | utils.py - misc. └───model │ │ common.py │ │ cain.py - main model | | cain_noca.py - model without channel attention | | cain_encdec.py - model with additional encoder-decoder └───data - implements dataloaders for each dataset │ | vimeo90k.py - main training / testing dataset | | video.py - custom data for testing │ └───symbolic links to each dataset | | ... 

Current version is tested on:

• Ubuntu 18.04
• Python==3.7.5
• numpy==1.17
• PyTorch==1.3.1, torchvision==0.4.2, cudatoolkit==10.1
• tensorboard==2.0.0 (If you want training logs)
• opencv==3.4.2
• tqdm==4.39.0

# Easy installation (using Anaconda environment) conda create -n cain conda activate cain conda install python=3.7 conda install pip numpy conda install pytorch torchvision cudatoolkit=10.1 -c pytorch conda install tqdm opencv tensorboard 

• We use Vimeo90K Triplet dataset for training + testing.
  • After downloading the full dataset, make symbolic links in data/ folder :
    • ln -s /path/to/vimeo_triplet_data/ ./data/vimeo_triplet
  • Then you're done!
• For more thorough evaluation, we built SNU-FILM (SNU Frame Interpolation with Large Motion) benchmark.
  • Download links can be found in the project page.
  • Also make symbolic links after download :
    • ln -s /path/to/SNU-FILM_data/ ./data/SNU-FILM
  • Done!

• First make symbolic links in data/ folder : ln -s /path/to/vimeo_triplet_data/ ./data/vimeo_triplet
  • Vimeo90K dataset
• For training: CUDA_VISIBLE_DEVICES=0 python main.py --exp_name EXPNAME --batch_size 16 --test_batch_size 16 --dataset vimeo90k --model cain --loss 1*L1 --max_epoch 200 --lr 0.0002
• Or, just run ./run.sh
• For testing performance on Vimeo90K dataset, just add --mode test option
• For testing on SNU-FILM dataset, run ./eval.sh
  • Testing mode (choose from ['easy', 'medium', 'hard', 'extreme']) can be modified by changing --test_mode option in eval.sh.

• Download pretrained models from [Here]
• Prepare frame sequences in data/frame_seq
• run test_custom.sh

If you find this code useful for your research, please consider citing the following paper:

@inproceedings{choi2020cain, author = {Choi, Myungsub and Kim, Heewon and Han, Bohyung and Xu, Ning and Lee, Kyoung Mu}, title = {Channel Attention Is All You Need for Video Frame Interpolation}, booktitle = {AAAI}, year = {2020} } 

Many parts of this code is adapted from:

• EDSR-Pytorch
• RCAN

We thank the authors for sharing codes for their great works.